/**
 * Copyright 2012 Brigham Young University
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package edu.byu.nlp.cluster.mom;

import edu.byu.nlp.cluster.em.Annealable;
import edu.byu.nlp.cluster.em.Expectable;

/**
 * @author rah67
 *
 */
public class EMAnnealable implements Annealable<MoMParameters> {

	private final double alpha;
	private final double beta;
	private final double[] alphaMinusOneAndObservedCounts;
	private final double[][] betaMinusOneAndObservedCounts;
	
	public EMAnnealable(double alpha, double beta, double[] alphaMinusOneAndObservedCounts,
			double[][] betaMinusOneAndObservedCounts) {
		this.alpha = alpha;
		this.beta = beta;
		this.alphaMinusOneAndObservedCounts = alphaMinusOneAndObservedCounts;
		this.betaMinusOneAndObservedCounts = betaMinusOneAndObservedCounts;
	}

	/** {@inheritDoc} */
	@Override
	public Expectable<MoMParameters> annealedExpectable(double temp) {
		return new EMExpectable(alpha, beta, alphaMinusOneAndObservedCounts,
				betaMinusOneAndObservedCounts, temp);
	}

}
